diff --git a/module3/exo3/exercice_fr_SARS-CoV-2.ipynb b/module3/exo3/exercice_fr.ipynb similarity index 94% rename from module3/exo3/exercice_fr_SARS-CoV-2.ipynb rename to module3/exo3/exercice_fr.ipynb index 72257017e4337dca35968011fcdf521ea5e5aacf..e62a3c56ded2c55e52a6782f3abfec7a29128cc9 100644 --- a/module3/exo3/exercice_fr_SARS-CoV-2.ipynb +++ b/module3/exo3/exercice_fr.ipynb @@ -3745,10 +3745,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "--> il y a donc bien des valeurs possiblement incoherentes avec des jours ou le taux cummule de cas decroit par rapport a la veille. \n", "Est-ce une rectification due a un mauvais diagnostique initial ? une modification de la methode de comptage ?\n", - "\n", - "Aux vues du faible nombre d'incoherence (362) par rapport au nombre de donnees total (288 lignes de donnees * 1142 comptage = 328896 donnees totales), on peut choisir de passer outre et de conserver la table ainsi\n", + "Quoi qu'il en soit, ce nombre d'incoherence (362) est tres faible par rapport au nombre de donnees total (288 lignes de donnees * 1142 comptage = 328896 donnees totales), et ne devrait pas impacter particulierement les conclusions qu'elles soient presentes ou non dans la table. \n", "\n", "\n", "\n", @@ -4132,8 +4130,9 @@ "\n", "\n", "### Creation d'un pays \"Hong-Kong\" \n", - "Hong-Kong apparait comme une province de la Chine. Pour plus de facilite a recupere les donnees, nous remplacons le pays anciennement \"China\" par Hong Kong pour la province Hong Kong uniquement. \n", - "Je choisis de faire une copie du fichier initial raw_data pour pouvoir y revenir le cas echeant. \n" + "Hong-Kong apparait comme une province de la Chine. Pour plus de facilite a recupere les donnees, nous remplacons le pays anciennement \"China\" par Hong Kong pour la province Hong Kong uniquement. .\n", + "\n", + "Je choisis de faire une copie du fichier initial pour pouvoir y revenir le cas echeant. \n" ] }, { @@ -4252,1010 +4251,30 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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60BeijingChina40.1824116.4142142236416880...40774.040774.040774.04077440774.040774.040774.0407744077440774
61ChongqingChina30.0572107.874069275775110...14715.014715.014715.01471514715.014715.014715.0147151471514715
62FujianChina26.0789117.98741510183559...17122.017122.017122.01712217122.017122.017122.0171221712217122
63GansuChina35.7518104.28610224714...1742.01742.01742.017421742.01742.01742.0174217421742
64GuangdongChina23.3417113.424426325378111151...103248.0103248.0103248.0103248103248.0103248.0103248.0103248103248103248
65GuangxiChina23.8298108.78812523233646...13371.013371.013371.01337113371.013371.013371.0133711337113371
66GuizhouChina26.8154106.8748133457...2534.02534.02534.025342534.02534.02534.0253425342534
67HainanChina19.1959109.7453458192233...10483.010483.010483.01048310483.010483.010483.0104831048310483
68HebeiChina39.5490116.130611281318...3292.03292.03292.032923292.03292.03292.0329232923292
69HeilongjiangChina47.8620127.761502491521...6603.06603.06603.066036603.06603.06603.0660366036603
70HenanChina37.8957114.90425593283128...9948.09948.09948.099489948.09948.09948.0994899489948
72HubeiChina30.9756112.270744444454976110581423...72131.072131.072131.07213172131.072131.072131.0721317213172131
73HunanChina27.6104111.708849244369100...7437.07437.07437.074377437.07437.07437.0743774377437
74Inner MongoliaChina44.0935113.94480017711...8847.08847.08847.088478847.08847.08847.0884788478847
75JiangsuChina32.9711119.4550159183347...5075.05075.05075.050755075.05075.05075.0507550755075
76JiangxiChina27.6140115.72212718183672...3423.03423.03423.034233423.03423.03423.0342334233423
77JilinChina43.6661126.1923013446...40764.040764.040764.04076440764.040764.040764.0407644076440764
78LiaoningChina41.2956122.6085234172127...3547.03547.03547.035473547.03547.03547.0354735473547
79MacauChina22.1667113.5500122256...3514.03514.03514.035143514.03514.03514.0351435143514
80NingxiaChina37.2692106.1655112347...1276.01276.01276.012761276.01276.01276.0127612761276
81QinghaiChina35.745295.9956000116...782.0782.0782.0782782.0782.0782.0782782782
82ShaanxiChina35.1917108.8701035152235...7326.07326.07326.073267326.07326.07326.0732673267326
83ShandongChina36.3427118.14982615274675...5880.05880.05880.058805880.05880.05880.0588058805880
84ShanghaiChina31.2020121.449191620334053...67040.067040.067040.06704067040.067040.067040.0670406704067040
85ShanxiChina37.5777112.29221116913...7167.07167.07167.071677167.07167.07167.0716771677167
86SichuanChina30.6171102.71035815284469...14567.014567.014567.01456714567.014567.014567.0145671456714567
87TianjinChina39.3054117.3230448101423...4392.04392.04392.043924392.04392.04392.0439243924392
88TibetChina31.692788.0924000000...1647.01647.01647.016471647.01647.01647.0164716471647
89UnknownChinaNaNNaN000000...1521816.01521816.01521816.015218161521816.01521816.01521816.0152181615218161521816
90XinjiangChina41.112985.2401022345...3089.03089.03089.030893089.03089.03089.0308930893089
91YunnanChina24.9740101.4870125111626...9743.09743.09743.097439743.09743.09743.0974397439743
92ZhejiangChina29.1832120.093410274362104128...11848.011848.011848.01184811848.011848.011848.0118481184811848
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33 rows × 1147 columns

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"80 Ningxia China 37.2692 106.1655 1 1 \n", - "81 Qinghai China 35.7452 95.9956 0 0 \n", - "82 Shaanxi China 35.1917 108.8701 0 3 \n", - "83 Shandong China 36.3427 118.1498 2 6 \n", - "84 Shanghai China 31.2020 121.4491 9 16 \n", - "85 Shanxi China 37.5777 112.2922 1 1 \n", - "86 Sichuan China 30.6171 102.7103 5 8 \n", - "87 Tianjin China 39.3054 117.3230 4 4 \n", - "88 Tibet China 31.6927 88.0924 0 0 \n", - "89 Unknown China NaN NaN 0 0 \n", - "90 Xinjiang China 41.1129 85.2401 0 2 \n", - "91 Yunnan China 24.9740 101.4870 1 2 \n", - "92 Zhejiang China 29.1832 120.0934 10 27 \n", - "\n", - " 1/24/20 1/25/20 1/26/20 1/27/20 ... 2/28/23 3/1/23 \\\n", - "59 15 39 60 70 ... 2275.0 2275.0 \n", - "60 36 41 68 80 ... 40774.0 40774.0 \n", - "61 27 57 75 110 ... 14715.0 14715.0 \n", - "62 10 18 35 59 ... 17122.0 17122.0 \n", - "63 2 4 7 14 ... 1742.0 1742.0 \n", - "64 53 78 111 151 ... 103248.0 103248.0 \n", - "65 23 23 36 46 ... 13371.0 13371.0 \n", - "66 3 4 5 7 ... 2534.0 2534.0 \n", - "67 8 19 22 33 ... 10483.0 10483.0 \n", - "68 2 8 13 18 ... 3292.0 3292.0 \n", - "69 4 9 15 21 ... 6603.0 6603.0 \n", - "70 9 32 83 128 ... 9948.0 9948.0 \n", - "72 549 761 1058 1423 ... 72131.0 72131.0 \n", - "73 24 43 69 100 ... 7437.0 7437.0 \n", - "74 1 7 7 11 ... 8847.0 8847.0 \n", - "75 9 18 33 47 ... 5075.0 5075.0 \n", - "76 18 18 36 72 ... 3423.0 3423.0 \n", - "77 3 4 4 6 ... 40764.0 40764.0 \n", - "78 4 17 21 27 ... 3547.0 3547.0 \n", - "79 2 2 5 6 ... 3514.0 3514.0 \n", - "80 2 3 4 7 ... 1276.0 1276.0 \n", - "81 0 1 1 6 ... 782.0 782.0 \n", - "82 5 15 22 35 ... 7326.0 7326.0 \n", - "83 15 27 46 75 ... 5880.0 5880.0 \n", - "84 20 33 40 53 ... 67040.0 67040.0 \n", - "85 1 6 9 13 ... 7167.0 7167.0 \n", - "86 15 28 44 69 ... 14567.0 14567.0 \n", - "87 8 10 14 23 ... 4392.0 4392.0 \n", - "88 0 0 0 0 ... 1647.0 1647.0 \n", - "89 0 0 0 0 ... 1521816.0 1521816.0 \n", - "90 2 3 4 5 ... 3089.0 3089.0 \n", - "91 5 11 16 26 ... 9743.0 9743.0 \n", - "92 43 62 104 128 ... 11848.0 11848.0 \n", - "\n", - " 3/2/23 3/3/23 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 \\\n", - "59 2275.0 2275 2275.0 2275.0 2275.0 2275 2275 \n", - "60 40774.0 40774 40774.0 40774.0 40774.0 40774 40774 \n", - "61 14715.0 14715 14715.0 14715.0 14715.0 14715 14715 \n", - "62 17122.0 17122 17122.0 17122.0 17122.0 17122 17122 \n", - "63 1742.0 1742 1742.0 1742.0 1742.0 1742 1742 \n", - "64 103248.0 103248 103248.0 103248.0 103248.0 103248 103248 \n", - "65 13371.0 13371 13371.0 13371.0 13371.0 13371 13371 \n", - "66 2534.0 2534 2534.0 2534.0 2534.0 2534 2534 \n", - "67 10483.0 10483 10483.0 10483.0 10483.0 10483 10483 \n", - "68 3292.0 3292 3292.0 3292.0 3292.0 3292 3292 \n", - "69 6603.0 6603 6603.0 6603.0 6603.0 6603 6603 \n", - "70 9948.0 9948 9948.0 9948.0 9948.0 9948 9948 \n", - "72 72131.0 72131 72131.0 72131.0 72131.0 72131 72131 \n", - "73 7437.0 7437 7437.0 7437.0 7437.0 7437 7437 \n", - "74 8847.0 8847 8847.0 8847.0 8847.0 8847 8847 \n", - "75 5075.0 5075 5075.0 5075.0 5075.0 5075 5075 \n", - "76 3423.0 3423 3423.0 3423.0 3423.0 3423 3423 \n", - "77 40764.0 40764 40764.0 40764.0 40764.0 40764 40764 \n", - "78 3547.0 3547 3547.0 3547.0 3547.0 3547 3547 \n", - "79 3514.0 3514 3514.0 3514.0 3514.0 3514 3514 \n", - "80 1276.0 1276 1276.0 1276.0 1276.0 1276 1276 \n", - "81 782.0 782 782.0 782.0 782.0 782 782 \n", - "82 7326.0 7326 7326.0 7326.0 7326.0 7326 7326 \n", - "83 5880.0 5880 5880.0 5880.0 5880.0 5880 5880 \n", - "84 67040.0 67040 67040.0 67040.0 67040.0 67040 67040 \n", - "85 7167.0 7167 7167.0 7167.0 7167.0 7167 7167 \n", - "86 14567.0 14567 14567.0 14567.0 14567.0 14567 14567 \n", - "87 4392.0 4392 4392.0 4392.0 4392.0 4392 4392 \n", - "88 1647.0 1647 1647.0 1647.0 1647.0 1647 1647 \n", - "89 1521816.0 1521816 1521816.0 1521816.0 1521816.0 1521816 1521816 \n", - "90 3089.0 3089 3089.0 3089.0 3089.0 3089 3089 \n", - "91 9743.0 9743 9743.0 9743.0 9743.0 9743 9743 \n", - "92 11848.0 11848 11848.0 11848.0 11848.0 11848 11848 \n", - "\n", - " 3/9/23 \n", - "59 2275 \n", - "60 40774 \n", - "61 14715 \n", - "62 17122 \n", - "63 1742 \n", - "64 103248 \n", - "65 13371 \n", - "66 2534 \n", - "67 10483 \n", - "68 3292 \n", - "69 6603 \n", - "70 9948 \n", - "72 72131 \n", - "73 7437 \n", - "74 8847 \n", - "75 5075 \n", - "76 3423 \n", - "77 40764 \n", - "78 3547 \n", - "79 3514 \n", - "80 1276 \n", - "81 782 \n", - "82 7326 \n", - "83 5880 \n", - "84 67040 \n", - "85 7167 \n", - "86 14567 \n", - "87 4392 \n", - "88 1647 \n", - "89 1521816 \n", - "90 3089 \n", - "91 9743 \n", - "92 11848 \n", - "\n", - "[33 rows x 1147 columns]" + "(33, 1147)" ] }, - "execution_count": 15, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_china = new_data.loc[(new_data['Country/Region'] == \"China\")]\n", - "df_china\n" + "df_china.shape\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "On somme toutes les donnes et on reinitialise les province, lattitude, longitude a NA, le pays a China.\n", + "On somme toutes les donnes pour les 33 provinces de Chine et on reinitialise les province, lattitude, longitude a NA, le pays a China.\n", "\n", "On travaille sur une Serie pandas, on la reformate en dataframe avec une tranposition. " ] @@ -6855,7 +5874,7 @@ "source": [ "### Analyse de l'évolution du nombre de cas cumulés au cours du temps\n", "\n", - "On transforma la table pour etre plus comprehensible par matplotlib pour faire le graphique - globalement on realise une transposition en supprimant les data lattitude/longitude pour le moment et en renommant les colonnes avec le nom du pays correspondant.\n" + "On transforma la table pour etre plus comprehensible par matplotlib pour faire le graphique. Globalement on realise une transposition en supprimant les data lattitude/longitude pour le moment et en renommant les colonnes avec le nom du pays correspondant.\n" ] }, { @@ -7257,7 +6276,7 @@ "source": [ "C'est donc les USA ayant eu le plus de cas recences, mais a normaliser par le nombre d'habitant global de chaque territoire et/ou du nombre de deces. \n", "\n", - "## Question subsidiaire\n", + "## Question subsidiaire - utilisqtion des donnees de deces\n", "\n", "On recupere les donnees de deces en faisant une copie local au besoin. " ] diff --git a/module3/exo3/exercice_fr.pdf b/module3/exo3/exercice_fr.pdf new file mode 100644 index 0000000000000000000000000000000000000000..343f91454cfabd8e885684f5070d8ed576682e77 Binary files /dev/null and b/module3/exo3/exercice_fr.pdf differ